DIAGNOSTICS AND ENHANCEMENTS TO THE NOAH

LSM SNOW MODEL

By: Ben Livneh

With contributions from: Youlong Xia, Kenneth E. Mitchell, Michael B. Ek, and Dennis P. Lettenmaier 1

Presentation Overview

• Motivation for the project.• Model background• Problem description• Nature of model extensions• Off-line testing and results• Conclusions

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Motivation• Snow acts as a natural reservoir/buffer, to

redistribute winter precipitation later into spring and summer when it is needed, important for water-supply, wildlife, etc…

• Snow cover has a significant impact on numerical weather prediction; higher reflectance, altered surface thermal regime.

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Seasonal ReservoirSeasonal Reservoir Man-made ReservoirMan-made Reservoir

Background – NOAH LSM• NOAH LSM: (NCEP, OSU, Air Force, Hydrologic

research lab)

• Land scheme for suite of weather and atmospheric models (NOAA), most versions of WRF. • Partitioning of radiation (H, LE)• Characterizes soil moisture, snow cover, etc…• Focus: snow cover very important to both land and atmosphere.

Problem• Negative in modeled

snow water equivalent (SWE) bias noted in Noah LSM.

• Important for coupled modeling (partitioning radiative inputs, fluxes) as well as hydrology (soil moisture anomalies and streamflow timing). Prompted offline testing of various model components

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Observed SWE (mm)

Noa

h S

WE

(m

m)

Mitchell et al, 2004Mitchell et al, 2004

Snow model structure (‘Control’: Noah v.2.7.1)• Single-layer snow model.• Linearized energy balance computation (computes skin

temperature).• No liquid water retention• Uses a satellite-based spatially varying maximum snow albedo

αMAX-satellite (Robinson and Kukla, 1985), constant value.• Turbulent exchange embodied in a surface exchange

coefficient: CH• Partial snow coverage:

T1, WMAX αMAX , ρ, CH

Areal Depletion

Snow cover frac.

SWE/WMAX

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• Snowpack Energy Balance = Qn + Qh + Qe + Qg + Qa

Model Extensions1. Evaluation of a snow albedo decay scheme that captures the

varying characteristics of the snowpack, currently not addressed in the control model;

2. Implementation of an algorithm that accounts for liquid water storage and refreeze within the pore space of the snowpack; a process that is not represented in the control.

• In addition, we report more limited sensitivity on: (a) Sensitivity of fractional snow coverage thresholds as they

interact with albedo; (b) An alternative stability correction to the turbulent

exchange physics.

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Snow Albedo Decay Scheme

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Snow Albedo• CONTROL model considers a fixed maximum snow albedo value,

based on the conversion of scene brightness (satellite imagery).• Does not account for seasonal variability, changes to the snow

surface, etc..

Control Noah αMAX-satellite converted from DMSP satellite imagery (1979)

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DMSP Satellite image, 1979

Defense Meteorological Satellite Program (DMSP)

Snow Albedo Decay Scheme• Snow albedo decay phenomenon confirmed by numerous studies (e.g Warren

et al., 1980). Rate is determined by character of snow surface (metamorphism, recrystalization, debris, etc…).

• One method to quantify decay, via age of snow surface (equation).• Seasonal decay rate (lower left) is faster during melt season: • Shift between seasonal curves determined by snow surface temperature.

Albedo decay scheme; based on Corps of Engineers, 1956; CA

tBsnow A max

Accumulation Season

Melt Season

αMAX ≈ 0.85;t: days since last snowfall;A,B: constants.

Equation:

10Warren, S. G. and W. J. Wiscombe, 1980: A model for the spectral albedo of snow, II, Snow containing atmosphenc aerosols, J. Atmos. Sci., 37, 2734-2745.

U.S. Army Corps Of Engineers, 1956: Summary report of the snow investigations, "Snow Hydrology", U. S. Army Engineer Division, North Pacific, 210 Custom House, Portland, Oregon 97209.

Snow Albedo – Additional Considerations

• NCEP desire to preserve spatial variability of satellite αMAX value for decay scheme.

• Hence an average value was used (midpoint between αMAX-

satellite and 0.85: weighting factor C = 0.5)

• Required constraining lower bound of albedo (α ≥ 40%)A: Accumulation season curve

(αMAX = 0.85)

B: Melt season curve (αMAX = 0.85)

C: Accumulation season curve (C = 0.5)

D: Melt season curve (C = 0.5)E: αMAX-satellite = constant (e.g. 52%)

Lower bound 11

Liquid Water Refreeze

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Liquid Water Refreeze• Important physical aspect (prevents snowmelt from going

directly to runoff).• Requires extending the snowpack energy balance, utilizing a

melt energy (cold content) approach:• Qm = Qn + Qh + Qe + Qg + Qa- ΔCC [W/m2]Where:

Qm = Energy available for melt;Qn = Net radiation fluxQh = Sensible heat fluxQe = Latent heat fluxQg = Ground flux Qa = Advected heat fluxΔCC = Change in internal energy of the snow pack (cold content) → *New Term: Allows for monitoring of thermal inertia between successive time steps; necessary for controlling melt and refreeze processes; involves new variable: Tpack 13

*Key difference in new energy balance

Liquid Water Storage

• Irreducible saturation, Swi, the maximum amount of liquid water a snowpack can hold, beyond which it will transmit water.

• Tested several quantities (3.5 % SWE, 3 % total snowdepth, 4% of snowpack p.v.)

• 4% of snow p.v. selected.

Where: SWE = snow water equivalent; dsnow = snow depth, φ = porosity, ρ = density

* Based on observations, Denoth et al., 2003

ice

pack

packwi

snowsnowwi

packwi

SWES

dS

vpofS

104.0

04.0

..%4

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Additional Sensitivities

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Additional Sensitivities1. Adjust areal depletion threshold for SWE; forest vs. non-forest.• *Related to recent collaborative work at the University of Arizona.

* Wang Z, Zeng X (2009) Evaluation of snow albedo in land models for weather and climate studies. Journal of Applied Meteorology and Climatology: In Press

• Wmax thresholds in control model: 0.04 m, 0.02 m (forest, non-forest)

• In a *recent paper (based on comparison with albedo measurements) proposes:0.20 m, 0.01 m (forest, non-forest)

• This work combined with the great disparity in satellite based albedo formed the basis for sensitivity testing.

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Additional Sensitivities

2. Stability correction for turbulent exchange scheme to prevent excessive sublimation from occurring during stable conditions.

• Extension of Slater et al. (2007) correction to momentum transfer (when RiB > 0)

CH = CH*(1-RiB/2)

CM = CM*(1-RiB/2)

RiB: dimensionless form of bulk richardson number

CH,CM: exchange coefficients for heat and momentum transfer, respectively.17

Model Evaluation Strategy

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Model evaluation strategy

• Model performance evaluated through comparisons of off-line model simulations with observations.

• 3 SNOTEL sites were chosen (western U.S.) to provide a reasonable cross section of snow types (continental, maritime, and intermediate) to represent regions where snow melt provides significant streamflow contributions (hydrology)

• Large-scale model performance was evaluated using spatial plots of cumulative snow covered days (SCE) as compared to satellite data over CONUS domain at 1/8⁰. Duration of snow cover plays a vital role in partitioning atmospheric inputs (coupled model applications)

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Results

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Albedo Decay

• Improvements in SWE peak timing and magnitude at SNOTEL sites

• Large-scale performance not consistent; generally improved at higher-elevation sites, however, highly correlated with satellite-based max snow albedo value (shown on next slide)

αMAX = 0.85

Comparison between albedo decay scheme (C=0.5) and control model 21

αMAX → C=0.5

αMAX-satellite

Albedo Decay• Correlation between (a) albedo decay

model performance vs. ‘control’, (b) αMAX-

satellite , and (c) vegetation type.

• For areas of high αMAX-satellite: albedo decay (C=0.5) case often falls below αMAX-satellite, yielding fewer snow covered days.

• Relationship between areal depletion, αMAX and vegetation discussed later.

(a) (b)

(a)

(b) (c) Depiction of Noah forest vegetation classes 22

Melt Refreeze• Improved quantity and

duration of SWE at SNOTEL sites.

• Large-scale improvements nearly ubiquitous, however control out-performs the melt-refreeze version for some low albedo cases.

• Altered energy balance and melt decision structure produce a slightly higher skin temperature in these cases, which can yield fewer snow covered days.

W.C. = 4% p.v.

W.C. = 3.5% SWE

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Comparison between melt/refreeze model and control model

Fractional Snow Coverage

• Objective: test alternate Wmax values which accentuate the difference between forest and grasslands (including albedo-decay) to obtain a reasonable match with satellite values.

• This was done by computing an average snow covered albedo for each grid-cell over the study period and comparing them with the CONTROL satellite-based snow albedo

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Fractional Snow Coverage• Time-integrated

plots less continuous than satellite value.

• Wang and Zeng (2009) Wmax values together with albedo decay (C=0.5) yield a reasonable match.

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Turbulent Exchange – Stability Correction

• Reduction in sublimation over portions of the domain. However, high elevation sites (SNOTEL) results are inconclusive.

• Further refinements appear to be in order.

• Stability correction was considered as part of the ‘control’.

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Comparison between stability corrected model and uncorrected model

Optimal Model Performance• Inclusion of albedo

decay (C=0.5), liquid water refreeze (W.C. = 4% p.v.) and adjusted Wmax

values.• Additionally,

provide for separate albedos during partial snow coverage

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Comparison between suggested model extensions and control model

Conclusions

• Negative SWE bias was addressed to improve model processes, evaluated at point-scale and large-scale.

• Point scale testing revealed that albedo decay and melt-refreeze processes improve the quantity and timing of SWE (serve to improve streamflow predictions in snowmelt dominated basins)

• Large-scale evaluation generally showed improvements in duration of snow cover; adjustment of areal depletion thresholds also provided for reasonable snow covered albedo estimates over the CONUS domain.

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Acknowledgements

Youlong Xia, Kenneth E. Mitchell, Michael B. Ek (NCEP)John Schaake (NWS)Ming Pan (Princeton)CPPA funding

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THANK YOU

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Livneh, B., Y. Xia, K.E. Mitchell, M.B. Ek, and D.P. Lettenmaier, 2010: Noah LSM Snow Model Diagnostics and Enhancements, J. of Hydrometeorology, in press

New Snowpack Energy Balance Solution Procedure

Qm = Qn + Qh + Qe + Qg + Qa- ΔCC• Procedure:1. Guess *snowpack temperature (e.g. 273.15⁰K)

for current time step and compute fluxes based on this temperature;

2. If residual Qm > 0 (energy surplus): melt an equivalent depth of water. First fill liquid water capacity within the pack; if melt water exceeds storage capacity, send to runoff.

3. If residual Qm < 0 (energy defecit): refreeze pack water, then cool pack with remaining energy deficit, if any.

* Snowpack temperature is a new variable31